The present disclosure relates generally to workforce management in a contact center, and, more particularly, to optimal scheduling of resources in the contact center.
In a contact center, the human agents typically represent a large percentage of the operating cost. As a result, an efficient workforce management system, particularly with respect to optimal scheduling of resources, has become an increasingly important component of effective contact center management. Existing contact centers typically track each resource's skillset(s) and utilize data from one or more sources, including historical data captured from automatic call distribution (ACD) systems in the contact center, to predict future staffing needs and the associated resource skillset mix that will be required to service incoming contacts. An inaccurate prediction may result in a resource roster with too many resources, which increases labor cost. Inaccurate predictions may also result in too few resources with the appropriate skills being scheduled or too few resources overall, both of which may severely impact service levels, increase customer frustration, and generally degrade the customer experience.
Modern, attribute-based contact centers assign one or more attributes to each incoming contact and make routing decisions based on matching of the contact attributes with the attributes of the resources. Information related to these contact attributes may be incorporated into staffing predictions, which results in better forecasting of contact center staffing needs and better matching between incoming contacts and the resources available for assignment.
While the increased granularity of an attribute-based contact center may result in more accurate forecasts of incoming contacts and staffing needs, the practicality of scheduling resources in the contact center may result in significant compromises being made with the resource roster, such that some benefits of the attribute-based model may not be realized. For example, an attribute-based contact center may be able accurately predict that contacts with a certain set of attributes are more likely to occur at one point during the workday, e.g., early morning, and that the contact type will change to a different mix of attributes during a different part of the workday, e.g., late morning. Because resource shifts at a contact center typically consist of at least a half day (four hours), it is often impractical to create a resource roster that may be changed dynamically to exactly match this prediction.